Design Cycle Time Research Articles

C-ShipGen: a conditional guided diffusion model for parametric ship hull design

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.

Experimental development of lightweight manipulators with improved design cycle time that leverages off-the-shelf robotic arm components.

The growing market for lightweight robots inspires new use-cases, such as collaborative manipulators for human-centered automation. However, widespread adoption faces obstacles due to high R&D costs and longer design cycles, although rapid advances in mechatronic engineering have effectively narrowed the design space to affordable robot components, turning the development of lightweight robots into a component selection and integration challenge. Recognizing this transformation, we demonstrate a practical framework for designing lightweight industrial manipulators using a case-study of indigenously developed 5 Degrees-of-Freedom (DOF) cobot prototype. Our framework incorporates off-the-shelf sensors, actuators, gears, and links for Design for Manufacturing and Assembly (DFMA), along with complete virtual prototyping. The design cycle time is reduced by approximately 40% at the cost of cobot real-time performance deviating within 2.5% of the target metric. Our physical prototype, having repeatability of 0.05mm calculated as per the procedure defined in ISO 9283:1998, validates the cost-effective nature of the framework for creating lightweight manipulators, benefiting robotic startups, R&D organizations, and educational institutes without access to expensive in-house fabrication setups.

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue.

Cfd Tools as Applied to Indian Combat Aircraft Configuration-an Experience

Computational Fluid Dynamics (CFD) tools are extensively used by aircraft industries all over the world in design and development of airplanes, with emphasis on reducing both the cost and design cycle time. This paper deals with the use of CFD tools at Aeronautical Development Agency (ADA) in design and development of Indian combat aircraft. The applications include: (1) evaluation of incremental changes in aerodynamic configuration, (2) wave drag evaluation, (3) aerodynamic loads, (4) flow field around air data sensor locations, (5) Euler calculation for coupled internal and external flow field, and (6) viscous computation of high angles of attack flows. In the initial design phase, codes based on the potential theory were used for wing design and fuselage shape optimization. Later indigenously developed Euler and Navier-Stokes codes were employed for the various applications. This paper brings out the strength and weakness of CFD as experienced at ADA and its comparison with the Wind Tunnel and Flight Data.

Role of Computational Fluid Dynamics in the Design of Aerospace Configurations

Computational Fluid Dynamics (CFD), a mature discipline now, can contribute considerably to the design, analysis and development of engineering systems involving fluid flows. Visualization of flow-field, surface load distribution and various aerodynamic forces and moments are the criteria for basic design of aerospace configurations. CFD complements experimental and theoretical fluid dynamics by providing an alternative and cost effective means to simulate real flow phenomena. The main advantage lies in its ability to cut down the number of wind-tunnel tests leading to reduction in the design cycle time and design cost. After a brief introduction to CFD, the role played by the modern CFD tools developed at the Computational and Theoretical Fluid Dynamics Division of National Aerospace Laboratories, Bangalore in the design and analysis of Aerospace configurations will be discussed here.

Iron nanoparticles decorated on graphitic carbon nitride/ZlF-67 support: An electro catalyst for the oxidation of methanol in fuel cell

Iron nanoparticles (FeNPs) are the tiniest particle of iron metal with a large surface area and high reactivity. They are non-toxic. FeNPs have excellent dimensional stability and also possess high thermal and electrical conductivity, high surface area, and are highly magnetic. FeNPs can oxidize immediately when exposed to water or air and produces free Fe ions. There are numerous applications of FeNPs but the most promising one includes their role in drug delivery. Considerable attention is being paid to the utilization of computer-based and modeling optimization in fuel cell systems design. One advantage of this method is the positive effect on high cost and design cycle time savings, as well as its improved operation and design. The performance of optimum development depends primarily on the method by which the prototype is developed. It is crucial to identify the important factors and those that can be compromised without having an adverse effect on the design. Modeling is carried out to capture the designer’s interest aspects of the fuel cell system. A mathematical model that represents particular fuel cell system aspects and estimates its characteristics can be in a form of algebraic equations, differential equations, or a process or subroutine based on a computer. The model can involve various alternatives to the design that can be achieved by changing parameters, variables, constraints or conditions. The principle explained in the preceding step contributes to the basis for comparing the various alternatives to design.

DEVELOPMENT OF A NOVEL DIABETIC FOOT RISK ASSESSMENT FOR LOW-RESOURCE HEALTHCARE SETTINGS: A CASE STUDY OF LEAN DESIGN

AbstractInterest in applying a “lean” philosophy to design has been slowly growing in recent years, but there are still few broadly applicable methodologies and illustrative cases published in the literature to guide lean design processes. Lean approaches promise cost reduction and increased product value, which could be particularly beneficial in product development for low- and middle-income country markets, where value demands are high. We use the clinical need of efficient diabetic foot risk assessment in lowresource healthcare settings to present an example of lean early-stage design of a medical device. The background of existing medical literature and commercial products is intentionally leveraged throughout the design process to streamline development and minimize the need for independent validation of product strategies and design features. Our approach resulted in an efficient design process that generated a novel, purely mechanical plantar pressure evaluation device that can indicate high risk of diabetic foot ulcer in resource-constrained settings. This case provides a practical example of how design processes can be adapted to be leaner where there are large gains from minimizing design cycle time and cost.

Predicting textural attributes and frictional characteristics of topical formulations based on non-linear rheology

A number of analytical procedures (Spectrum Descriptive Analysis (SDA), Quantitative Descriptive Analysis (QDA), and Check All That Apply (CATA)) are used for characterizing sensorial attributes of topical formulations. However, these techniques are evaluated by expert panels/consumers, which are subjective, expensive and time consuming. Despite widespread use of these methods, the techniques do not necessarily aid in the development of innovative formulations and understanding of consumer liking. In addition, the hedonic attributes of a product can significantly dominate the sensation. Therefore, a more rapid, quantitative, and objective approach is required for understanding the formulation factors that governs sensory perception at different points of consumer application. Rheological evaluations of topical formulations were carried out under steady and oscillatory (SAOS and LAOS) rheology using a commercial rheometer. Friction measurements were performed using an in-house built tribometer on non-biological skin model to investigate how surface properties are influenced by application of different topical formulations. Further, a broad range of instrumental texture measurements was performed to characterize the formulations. Principal component analysis was used for dimensionality reduction of the instrumental data. Supervised machine learning was performed on the closely related PCA parameters to develop predictive models. We identified physical parameters relevant to different perceptual attributes by comparing a range of commercial topical formulations with various compositions using rheological and tribological methods. Multi-variate machine learning models were developed where several key instrumental textural attributes and skin feel could be predicted from data obtained from linear and non-linear rheological measurements. Our study shows rheological analysis based on a few material parameters can be effectively used for predicting instrumental sensory attributes that is of relevance to consumer care industry. The machine learning models may benefit development of innovative formulations, valorisation of new materials and serve as a platform for mapping commercial formulations for product optimization. The models can likely shorten design cycle time by screening through large volume of formulations and short list only those with highest potential to be evaluated by a very specialized and expensive human test panel.

Using iMTGSPICE to Optimize Cascaded Miller OTAs and Boost Electrical Performance, Robustness and Reduce Die Area

Analog designers usually cascade several basic analog building blocks that were previously optimized to increase the open-loop voltage gain (AV0) and to avoid the increasing of the Design and Optimization Cycle Time (DOCT) of a given System-On-a-Chip. However, still exist a big doubt about if this approach is really better than that considering the implementation of amplifiers in cascade, without considering previously optimized analog building blocks. Thus, the main objective of this paper is to perform a detailed comparative study between robust amplifiers in cascade implemented without using analog building blocks previously designed and optimized and a similar one that uses analog building blocks previously designed and optimized (typical design approach). The computational design and optimization tool, named iMTGSPICE, which uses heuristics algorithms of the Artificial Intelligence integrated to the expertise of the analog designers (Human Intelligence), is used to perform these implementations to remarkably reduce DOCT of these implementations. This work demonstrates that it is possible to reduce the total gate area (44.6%) and to increase the operating temperature range from 0oC to 36oC to -40oC to 125oC of the robust amplifier in cascade that was implemented without using analog building blocks previously designed and optimized compared to one that was implemented by using analog building blocks previously designed and optimized.

Customized Imperialist Competitive Algorithm Methodology to Optimize Robust Miller CMOS OTAs

The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated and depend heavily on the designer’s experience, and are associated with very long design and optimization-cycle times. In addition, in order to the analog and radiofrequency (RF) CMOS IC work suitably in practice, it is necessary to perform robustness analyses (RAs) through Simulation Program with Integrated Circuit Emphasis (SPICE) simulations, which result in still-higher design and optimization cycle times and therefore represent the biggest bottleneck to the launching of new electronic products. In this context, this manuscript aims to present, for the first time, the use of a custom imperialist competitive algorithm (ICA) in order to reduce the design and optimization-cycle times of analog CMOS ICs. In this study, we implement some Miller CMOS operational transconductance amplifiers (OTAs) using the computational tool named iMTGSPICE, considering two different bulk CMOS IC manufacturing processes from Taiwan Semiconductor Company (TSMC) (180 nm and 65 nm nodes) and two evolutionary optimization methodologies of artificial intelligence, i.e., ICA and a genetic algorithm (GA). The main result obtained by this work shows that, by using an ICA-customized evolutionary algorithm to perform the design and optimization processes of Miller CMOS OTAs, it is possible to reduce the design and optimization-cycle times by up to 83% in relation to those implemented with the GA-customized evolutionary algorithm, achieving practically the same electrical performance.

Assessing Design Repository Search Effectiveness

Reuse of design knowledge in systems engineering (SE) has been identified as a potential way to decrease design cycle times and development costs of engineering projects. Design repositories (DR) have been proposed and demonstrated as a way to store design knowledge in a reusable way. Yet little attention has been devoted specifically to the search methods used to identifying useful information within a DR, or how search method effectiveness can be measured or assessed. This paper demonstrates a method for assessing the effectiveness of search approaches within DR. The use of this method in assessing multiple search algorithms is demonstrated by example. The assessment method presented herein can also be used to identify the characteristics of repository data or intended reuse applications that affect the performance of search methods. These results can be used by engineers to inform selection of search methods based on anticipated future DR use cases.

Integrated DC - DC converter design methodology for design cycle speed up

A novel design methodology, enabling extreme design cycle time speed up of DC - DC power converters, is developed. The concept is based on providing high accuracy post-layout RC parasitics aware results, by replacing complicated large RC netlists with small signal approximation models. Scattering parameters analysis is adopted for “on the fly” performance simulation of the power MOSFET switches' routings, which act as large passive linear networks. The RC parasitics aware back end of line (BEOL) S-parameter model is extracted and seamlessly integrated into the schematic testbench, considering the actual circuit as a black box and therefore actively cutting down the design's netlist size to minimum values. Thus, the DC – DC converter performance degradation, that previously could not be simulated, now is accurately predicted and evaluated while the respective simulation time and the number of design iterations needed from layout (physical design) to the schematic and vice versa, are minimized. The proposed methodology is validated using an Integrated Pulse Width Modulation controlled DC - DC converter product vehicle, for light energy harvesting applications, designed, simulated and fabricated in a 0.18 μm CMOS standard process. Experimental results confirm the accuracy and design cycle speed up effectiveness of the proposed novel IC power converter design methodology.

Uncovering hidden capacity in overall equipment effectiveness management

An ongoing challenge in manufacturing management is to maximize the output volume from limited production capacity. Total productive maintenance (TPM) is a promising strategy to enhance machinery efficiency in factories by reducing downtime, speed, and quality losses. The associated metric of TPM is overall equipment effectiveness (OEE), which comprises three components, namely availability, performance, and quality, that measure the various aspects of production losses. However, output is restricted by the design cycle time in OEE, so purely improving OEE could only yield limited output increase. We argue that excess motion and processing in the ideal design cycle lead to design cycle losses. If these non-value-added activities as hidden capacity could be eliminated, production output would have considerable increment without extra investment. Adopting the input distance function approach, we show that the improvements gained from enhancing overall equipment availability and performance are different from the capacity unleashed by eliminating the design cycle losses. Accordingly, we propose value-added overall equipment effectiveness (VAOEE) as a novel metric to measure all the identified losses in search of hidden capacity. We provide three examples to demonstrate application of our novel productivity improvement approach in both semi-automatic and manual production in a non-continuous based plant.

Experimental and numerical evaluation of elastic properties of Bi-directional carbon epoxy composites

This paper presents the evaluation of properties like elastic moduli, shear moduli and poisson’s ratio as a function of volume fraction of fibres and angle of orientation of reinforcement. The simulation has been carried out for bidirectional carbon epoxy composites. Micromechanical models like Mori-Tanaka, Self-Consistent technique are used for predictions. The application of the method shows its capability in quick prediction of properties avoiding the dependence on experimental data and reducing the design cycle time. Carbon epoxy composites were fabricated and tested at various orientations with respect to principal reinforcement directions. A good match has been obtained between the simulation and the experimental data.

The Utility of Simulink Subsystems in Handling and Processing of Biomedical Signals and Images

To model, simulate, and analyze multi-domain dynamical systems, Simulink, which is a Matlab-based graphical programming environment, can be used effectively. Due to the drag-drop facility, accessible graphic user interface components, and zero coding environments, Simulink becomes the most used tool both in industry and academia. The design cycle time of any real-time systems can be reduced using Simulink than other software tools. This article focuses mainly on the utility behind the subsystems such as enabled subsystem, triggered subsystem, triggered and enabled subsystem, and control flow subsystem in biomedical signal and image processing. Image segmentation using enabled subsystem, voiced/unvoiced classification using triggered subsystem, and the computation of root-mean-square (RMS) amplitude using If Action subsystem are implemented using breast cancer image and human voice signal. The Matlab 9.4 tool is used for experimental simulation with the biomedical signals and images.

Multi-body simulation with notch stress analysis of an axle assembly

The optimization of mining vehicle rear axle weld joints is a complex procedure. The designs for low-cycle fatigue with peak loads were analyzed using computational models and rig testing. On-road evaluation of high-cycle fatigue was performed with the entire vehicle. The iterative analysis is used in design optimization to minimize design cycle time. In sophisticated fatigue calculations with all affecting factors, FEM is utilized. In fatigue failures using road load data, the present deterministic method yields up to 80% correlations. Some critical failure spots were most likely missed owing to operational variations caused by the randomness and multiaxiality of load transfer in link mechanisms with non-linear elements. The fabricated rear axle housing joints are optimized by comparing damage state estimates using stochastic type and multiaxial equivalent loads obtained using Multibody Simulation. The damage state analysis as MBS input stimulus in FE weld models and bolted designs were compared.

Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning

Abstract One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade. Design of turbine blades needs to consider multiple aspects like aerodynamic efficiency, durability, safety and manufacturing, which make the design process sequential and iterative. The sequential nature of these iterations forces a long design cycle time, ranging from several months to years. Due to the reactionary nature of these iterations, little effort has been made to accumulate data in a manner that allows for deep exploration and understanding of the total design space. This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely PMI (PMI), to carry out an explicit inverse design. PMI calculates the design explicitly without costly iteration and overcomes the challenges associated with ill-posed inverse problems. In this work the framework will be demonstrated on inverse aerodynamic design of three-dimensional turbine blades.

A Design Methodology Using the Inversion Coefficient for Low-Voltage Low-Power CMOS Voltage References

This paper presents an analog design methodology, which uses the selection of the inversion coefficient of MOS devices, to design low-voltage and low-power (LVLP) CMOS voltage references. The motivation of this work comes from the demand for analog design methods that optimize the sizing process of transistors working in subthreshold operation. The advantage of the presented method – compared to the traditional approaches for circuit design – is the reduction of design cycle time and the minimization of simulation iterations when the proposed equations are used. As a case study, a LVLP voltage reference based on subthreshold MOSFETs with a supply voltage of 0.7 V was designed in a 0.18-μm CMOS technology.

M2A2: Microscale Modular Assembled ASICs for High-Mix, Low-Volume, Heterogeneously Integrated Designs

With CMOS process technology scaling, the mask cost for fabricating nano-scale transistors, contacts, and interconnects has become prohibitively expensive, especially, for low volume designs. Moreover, higher transistor density has resulted in higher design complexity and large-sized die, which has led to an increase in the design cycle time and degradation in the process yield. These challenges are forcing low-volume application-specific integrated circuits (ASICs) toward highly suboptimal field-programmable gate arrays (FPGAs). In this article, we propose a new approach for designing and fabricating high-mix, low-volume heterogeneously integrated ASICs, referred to as Microscale Modular Assembled ASIC (M2A2), consisting of: 1) pick-and-place assembly of prefabricated blocks (PFBs) which utilizes the nano-precision placement capabilities developed in jet-and-flash imprint lithography (J-FIL) and 2) EDA design methodology utilizing unsupervised learning and graph-matching techniques. The EDA methodology leverages existing CAD tool infrastructure for easy adoption into the current EDA ecosystem. The proposed fabrication technology makes use of pick-and-place assembly technique to allow nano-precise assembly of PFBs. The PFBs can be fabricated in advanced process nodes and then knitted together on a wafer substrate. Custom-designed low-cost back-end metal layers can then be created/placed on top of the PFB knitted layer to realize a variety of high-mix, low-volume ASIC designs. M2A2 would allow more flexibility in front-end design by optimal PFB selection and knitting compared to the earlier proposed approaches such as structured ASICs (sASICs). In this article, the performance of M2A2-based designs are compared with different design technologies, such as baseline ASICs, FPGAs, and sASICs at 16 nm, 40 nm, and 130 nm CMOS process nodes. The post-PNR simulation results achieved over 15 IWLS benchmarks show that the proposed M2A2 designs achieve 27.11x -34.89x reduced power-delay-product (PDP) compared to FPGAs, and incur 1.69x -2.36x larger area compared to the baseline ASICs. The M2A2 designs achieve 15%-68.5% smaller area and 8.5%-52% higher performance compared to the sASIC methodologies. Moreover, the key fabrication steps in the proposed M2A2 technology are presented. The experimental fab results along with the proposed EDA flow simulations show promising results for the proposed M2A2 technology. Design tradeoffs and process challenges for large scale deployment of the M2A2 technology are discussed along with their mitigation strategies.

Automatic Circuit Design of CMOS Miller OTA Using Cuckoo Search Algorithm

The circuit design of the CMOS based analog part of a mixed-signal integrated circuit (IC) needs a large fraction of the overall design cycle time. The automatic design of an analog circuit is inevitable, seeing recently development of System-on-Chip (SOC) design. This brings about the need to develop computer aided design (CAD) tools for automatic design of CMOS based analog circuits. In this article, a Cuckoo Search (CS) algorithm is presented for automatic design of a CMOS Miller Operational Transconductance Amplifier (OTA). The source code of the CS algorithm is developed using the C language. The Ngspice circuit simulator has been used as a fitness function creator and evaluator. A script file is written to provide an interface between the CS algorithm and the Ngspice simulator. BSIM3v3 MOSFET models with 0.18 µm and 0.35 µm CMOS technology have been used to simulate this circuit. The simulation results of this work are presented and compared with previous works reported in the literature. The experimental simulation results obtained by the CS algorithm satisfy all desired specifications for this circuit.